Multi-Layer Model for Network Fault Detection Based on Artificial Immune

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Abstract:

In order to reduce the fault detection rate and improve the self-adaptive capability in network fault detection, an artificial immune mechanism which is inspired by multi-layer defense of a biological immune system is proposed to perform network fault detection. The immune model is composed of three parts: the inherent detection layer, fuzzy judgment layer and adaptive detection layer. Dendritic cells can influence the reaction of coordinating T-cells, which can be activate or tolerate so that induce adaptive immune responses and affirm the type of adaptive response. Inherent detection layer and fuzzy judgment layer interact with each other to reduce the error detection rate, while the adaptive detection layer is capable of learning unknown fault patterns.

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Periodical:

Advanced Materials Research (Volumes 219-220)

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219-222

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Online since:

March 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] J.Jamie Paul Twycross.Integrated Innate and Adaptive Artificial Immune Systems Applied to Process Anomaly Detection [D].University of Nottingham.(2007).

Google Scholar

[2] Yuling Tian. On diagnosis prototype system for motor faults based on immune model[C]. 2009 International Conference on Business Intelligence and Financial Engineering. (2009) pp.126-129.

DOI: 10.1109/bife.2009.38

Google Scholar

[3] Junmin Zhan,Yiwen Liang. Worm detection immune model integrating innate and adaptive immunity [J]. Computer Science. 36(12), (2009), pp.119-123.

Google Scholar